Mid ranging is an algorithm for controlling one control variable, such as flow or pressure, with two manipulated variables. Although mid ranging is a very well established technique there are a number of practical iss...
详细信息
Mid ranging is an algorithm for controlling one control variable, such as flow or pressure, with two manipulated variables. Although mid ranging is a very well established technique there are a number of practical issues which must be addressed when implementing mid ranging in the DCS. These issues include handicapped operation, bump less transfer and proper handling of saturation conditions. In this article, an algorithm for mid ranging is presented which exploits the inbuilt functionality of the control blocks in a modern DCS. A specific feature of the algorithm is the ability to maintain control even when one of the manipulated variables is out of service. Finally the article also demonstrates how the algorithm is extended to handle three manipulated variables. (C) 2016, IFAC (International Federation of Automatic control) Hosting, by Elsevier Ltd. All rights reserved.
Rising CO2 levels in the atmosphere has been a serious concern and threat to the environment. Thus mitigation of CO2 becomes really important. To achieve the same, a relatively new approach of tri-reforming process is...
详细信息
Rising CO2 levels in the atmosphere has been a serious concern and threat to the environment. Thus mitigation of CO2 becomes really important. To achieve the same, a relatively new approach of tri-reforming process is studied in this paper. Taking waste flue gases as a source of CO2 and methane as a co-feed, a process flow sheet has been developed that converts the above mentioned two species to methanol. In this study we consider the conversion of flue gases and methane into methanol via three steps namely the tri-reforming process, a water separation system and methanol production process. The water separation system used in conjunction with the other two processes is a novel aspect of our approach and it demonstrates the importance of water removal in terms of the overall flow sheet improvement. The improved process delivers an improved product yield of 3.12 times the original tri-reforming coupled methanol production process. The developed process flow sheet is simulated and optimized in Aspen Plus V8.4 and various sensitivity studies have been performed that illustrate the feasibility of the proposed approach Moreover an effective multiple stage methanol production process is suggested for CO? rich synthesis gas generated by tri-reforming or otherwise. (C) 2016, IFAC (International Federation of Automatic control) Hosting, by Elsevier Ltd. All rights reserved.
Crystals wade. of periodically well-ordered nano- and/or micro scale elements can interact with light to give novel properties. These perfect crystals have applications in a wide range of areas. For example, invisibil...
详细信息
Crystals wade. of periodically well-ordered nano- and/or micro scale elements can interact with light to give novel properties. These perfect crystals have applications in a wide range of areas. For example, invisibility cloaks that reroute light transmission make objects disappear. However, manufacturing such perfect, crystals still remains challenging. Here, we propose a low-dimensional Markov decision process based dynamic programming framework to optimally control a colloidal self-assembly process for perfect crystal fabrication. Based on the simulation results, we demonstrate that, an open-loop control policy identified with the proposed framework is able to reduce the defective assemblies from 46% of uncontrolled to 8% of controlled production. Moreover, when feedback is available, a closed-loop optimal finite-horizon control policy can further reduce the defective assemblies down to Foe out of 100 independent simulation runs. (C) 2016, IFAC (lnternational Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.
The main limitation of perturbation based extremum seeking methods is the requirement of a multiple time-scale separation between the system dynamics, the perturbation frequency, and the adaptation rate so as to avoid...
详细信息
The main limitation of perturbation based extremum seeking methods is the requirement of a multiple time-scale separation between the system dynamics, the perturbation frequency, and the adaptation rate so as to avoid interactions and possible instabilities. This causes the convergence to he extremely slow. In the present work, we propose a simple modification to the perturbation-based extremum seeking control method that can be used when the system cannot be accurately approximated by a Wiener-Hammerstein model for which convergence rate acceleration schemes are available. The linear filtering used in the perturbation based extremum seeking control for estimating the objective function gradient is replaced by a recursive least square with forgetting factor estimation algorithm. It is shown that this simple modification can accelerate convergence to the optimum by removing one time scale separation. (C) 2016, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.
In this paper, we present a novel data-driven approach for estimating plant-model mismatch for linear MIMO systems operating under constrained MPC. We begin with analyzing the closed-loop plant data;under the assumpti...
详细信息
In this paper, we present a novel data-driven approach for estimating plant-model mismatch for linear MIMO systems operating under constrained MPC. We begin with analyzing the closed-loop plant data;under the assumption that changes in the active set of constraints of the controller are due to (low frequency) setpoint, changes, we separate the data into a finite number of fixed active set (PAS) subsets, each of which features a time-invariant active set of MPC constraints. We establish an explicit relationship relating the magnitude of plant-model mismatch to the autocovariance of the system output in the FAS case, while accounting for changes in the setpoint value. The mismatch estimation problem is then formulated as a global optimization calculation, aimed at minimizing the discrepancy between the antocovariance estimated using this theoretical tool, and the autocovariance of plant outputs computed from operating data for each PAS subset,. A chemical process case study is presented to illustrate the effectiveness of the approach. (C) 2016, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.
In this paper, we present a general framework for robustness analysis for pressure control in managed pressure drilling (MPD). In particular, we apply the analysis to the pressure controller proposed in the work Godha...
详细信息
In this paper, we present a general framework for robustness analysis for pressure control in managed pressure drilling (MPD). In particular, we apply the analysis to the pressure controller proposed in the work Godhavn et al. (2011), based on which we also give an approach to search for controller tuning parameters with the goal of maximizing the robustness of system Stability and control performance to various sorts of uncertainties, disturbances and noise. The resulting tuning table can be used for online computation of the controller parameters. The method proves effective in a simulation study. (C) 2016, IFAC (International Federation of Antomatic control) Hosting by Elsevier Ltd. Ali rights reserved.
In this study, application of Lipschitz observers to the monitoring of cultures of micro-algae in photo-tors is investigated. To design the observer, the dynamic model has to be structured into an observable linear pa...
详细信息
In this study, application of Lipschitz observers to the monitoring of cultures of micro-algae in photo-tors is investigated. To design the observer, the dynamic model has to be structured into an observable linear part and a nonlinear Lipschitz part. A systematic method is proposed for the definition of the linear part, so as to ensure that it is stable and observable. The observer is tested in a real-life application, namely cultures of micro-algae Scenesdesmus obliquus, where the internal quota has to be estimated from either biomass measurements only or biomass and medium substrate concentrations. The Lipschitz observer shows robust performance, as compared to the extended Kalman filter. (C) 2016, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.
Thanks to the continuously increasing computational power of CPUs, nowadays Model Predictive control (MPC), initially designed for multi-variable] control of chemical plants, is adopted in a large number of different ...
详细信息
Thanks to the continuously increasing computational power of CPUs, nowadays Model Predictive control (MPC), initially designed for multi-variable] control of chemical plants, is adopted in a large number of different applications, covering a wide range of processdynamics ranging from slow to even fast time scales. As a consequence, a renewed interest in numerical optimization tools, especially for nonlinear systems, arose. This work aims at demonstrating the feasibility of a novel methodology, based on a Linear Fractional Transform (LFT) formulation of the system dynamics, that allows to efficiently solve nonlinear MPC problems. An application example, concerning the tracking control of an autonomous vehicle, shows the effectiveness of the proposal. (C) 2016, IFAC (International Federation of Automatic (control) Hosting by Elsevier Ltd. All rights reserved.
The concept of globally optimal controlled variable selection has recently been proposed to improve self-optimizing control performance of traditional local approaches. However, the associated measurement subset selec...
详细信息
The concept of globally optimal controlled variable selection has recently been proposed to improve self-optimizing control performance of traditional local approaches. However, the associated measurement subset selection problem has not be studied. In this paper, we consider the measurement subset selection problem for globally self-optimizing control (gSOC) of Tennessee Eastman (TE) process. The TE process contains substantial measurements and had been studied for SOC with controlled variables selected from individual measurements through exhaustive search. This process has been revisited with improved performance recently through a retrofit approach of gSOC. To extend the improvement further, the measurement subset selection problem for gSOC is considered in this work and solved through a modification of an existing partially bidirectional branch and bound (PB3) algorithm originally developed for local SOC. The modified PB3 algorithm efficiently identifies the best measurement candidates among the full set which obtains the globally minimal economic loss. Dynamic simulations are conducted to demonstrate the optimality of proposed results. (C) 2016, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved
Many biotechnological processes have optimal substrate concentrations where a maximum growth rate is achieved. In this work an extremum seeking controller and a gradient estimator are proposed to maximize the growth r...
详细信息
Many biotechnological processes have optimal substrate concentrations where a maximum growth rate is achieved. In this work an extremum seeking controller and a gradient estimator are proposed to maximize the growth rate in fed-batch bioprocesses even when the substrate to growth rate map is unknown. Both the controller and estimator are obtained with high order sliding mode algorithms. Stability proofs are given, and tools to tune the algorithms in terms of bounds of the hessian of the map are derived. Simulation results that illustrate the performance of the controller are shown. (C) 2016, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.
暂无评论